Tutorial 1: Working with OpenStreetMap#

Within this tutorial, we will explore the power of OpenStreetMap. We will learn how to extract information from OpenStreetMap, how you can explore and visualize this, and how to use it for some basic analysis.

Tutorial Outline

1. Introducing the packages
2. Extract and visualize land-use information from OpenStreetMap
3. Rasterize land-use information
4. Extracting buildings from OpenStreetMap
5. Analyze and visualize building stock
6. Extracting roads from OpenStreetMap
7. Plot Routes Using OpenStreetMap and Folium

Learning Objectives#

1.Introducing the packages#

Within this tutorial, we are going to make use of the following packages:

GeoPandas is a Python packagee that extends the datatypes used by pandas to allow spatial operations on geometric types.

OSMnx is a Python package that lets you download geospatial data from OpenStreetMap and model, project, visualize, and analyze real-world street networks and any other geospatial geometries. You can download and model walkable, drivable, or bikeable urban networks with a single line of Python code then easily analyze and visualize them. You can just as easily download and work with other infrastructure types, amenities/points of interest, building footprints, elevation data, street bearings/orientations, and speed/travel time.

NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Matplotlib is a comprehensive Python package for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.

Geocube is a Python package to convert geopandas vector data into rasterized data.

Rasterio is a Python package to organize and store gridded raster datasets such as satellite imagery and terrain models. Rasterio reads and writes these formats and provides a Python API based on Numpy N-dimensional arrays and GeoJSON.

We will first need to install these packages in the cell below. Uncomment them to make sure we can pip install them

#!pip install osmnx
#!pip install geopandas
#!pip install geocube
#!pip install contextily

And we will import these packages in the cell below:

import osmnx as ox
import networkx as nx
import contextily as cx

from matplotlib.colors import LinearSegmentedColormap
from matplotlib.patches import Patch
import matplotlib.pyplot as plt
from IPython.display import IFrame
from geocube.api.core import make_geocube

%matplotlib inline

2. Extract and visualize land-use information from OpenStreetMap#

The first step is to define which area you want to focus on. In the cell below, you will now read “Steenwijk, The Netherlands”. Change this to any random or municipality in the Netherlands that (1) you can think of and (2) will work.

In some cases, the function does not recognize the location. You could either try a different phrasing or try a different location. Many parts of the Netherlands should work.

place_name = "Steenwijk, The Netherlands"
area = ox.geocode_to_gdf(place_name)

Now let us visualize the bounding box of the area. As you will notice, we also estimate the size of the area. If the area size is above 50km2, or when you have many elements within your area (for example the amsterdam city centre), extracting the data from OpenStreetMap may take a little while.

area_to_check = area.to_crs(epsg=3857)
ax = area_to_check.plot(figsize=(10, 10), color="none", edgecolor="k", linewidth=4)
ax.set_xticks([])
ax.set_yticks([])
ax.set_axis_off()
cx.add_basemap(ax, zoom=14)

size = int(area_to_check.area/1e6)

ax.set_title("{}. Total area: {} km2".format(place_name,size),fontweight='bold')

Text(0.5, 1.0, 'Steenwijk, The Netherlands. Total area: 31 km2')

Now we are satisfied with the selected area, we are going to extract the land-use information from OpenStreetMap. To find the right information from OpenStreetMap, we use tags.

As you will see in the cell below, we use the tags “landuse” and “natural”. We need to use the “natural” tag to ensure we also obtain water bodies and other natural elements.

tags = {'landuse': True, 'natural': True}   
landuse = ox.geometries_from_place(place_name, tags)

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[5], line 2
tags = {'landuse': True, 'natural': True}   
----> 2 landuse = ox.geometries_from_place(place_name, tags)

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/osmnx/geometries.py:231, in geometries_from_place(query, tags, which_result, buffer_dist)
utils.log("Constructed place geometry polygon(s) to query API")
# create GeoDataFrame using this polygon(s) geometry
--> 231 gdf = geometries_from_polygon(polygon, tags)
return gdf

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/osmnx/geometries.py:278, in geometries_from_polygon(polygon, tags)
   raise TypeError(
       "Boundaries must be a shapely Polygon or MultiPolygon. If you requested "
       "geometries from place name, make sure your query resolves to a Polygon or "
       "MultiPolygon, and not some other geometry, like a Point. See OSMnx "
       "documentation for details."
   )
# download the geometry data from OSM
--> 278 response_jsons = downloader._osm_geometries_download(polygon, tags)
# create GeoDataFrame from the downloaded data
gdf = _create_gdf(response_jsons, polygon, tags)

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/osmnx/downloader.py:584, in _osm_geometries_download(polygon, tags)
for polygon_coord_str in polygon_coord_strs:
   query_str = _create_overpass_query(polygon_coord_str, tags)
--> 584     response_json = overpass_request(data={"data": query_str})
   response_jsons.append(response_json)
utils.log(
   f"Got all geometries data within polygon from API in {len(polygon_coord_strs)} request(s)"
)

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/osmnx/downloader.py:773, in overpass_request(data, pause, error_pause)
utils.log(f"Post {prepared_url} with timeout={settings.timeout}")
headers = _get_http_headers()
--> 773 response = requests.post(
   url, data=data, timeout=settings.timeout, headers=headers, **settings.requests_kwargs
)
sc = response.status_code
# log the response size and domain

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/requests/api.py:115, in post(url, data, json, **kwargs)
def post(url, data=None, json=None, **kwargs):
   r"""Sends a POST request.

   :param url: URL for the new :class:`Request` object.
   (...)
   :rtype: requests.Response
   """
--> 115     return request("post", url, data=data, json=json, **kwargs)

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/requests/api.py:59, in request(method, url, **kwargs)
# By using the 'with' statement we are sure the session is closed, thus we
# avoid leaving sockets open which can trigger a ResourceWarning in some
# cases, and look like a memory leak in others.
with sessions.Session() as session:
---> 59     return session.request(method=method, url=url, **kwargs)

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/requests/sessions.py:587, in Session.request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
send_kwargs = {
   "timeout": timeout,
   "allow_redirects": allow_redirects,
}
send_kwargs.update(settings)
--> 587 resp = self.send(prep, **send_kwargs)
return resp

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/requests/sessions.py:701, in Session.send(self, request, **kwargs)
start = preferred_clock()
# Send the request
--> 701 r = adapter.send(request, **kwargs)
# Total elapsed time of the request (approximately)
elapsed = preferred_clock() - start

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/requests/adapters.py:489, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
try:
   if not chunked:
--> 489         resp = conn.urlopen(
           method=request.method,
           url=url,
           body=request.body,
           headers=request.headers,
           redirect=False,
           assert_same_host=False,
           preload_content=False,
           decode_content=False,
           retries=self.max_retries,
           timeout=timeout,
       )
   # Send the request.
   else:
       if hasattr(conn, "proxy_pool"):

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/urllib3/connectionpool.py:703, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
   self._prepare_proxy(conn)
# Make the request on the httplib connection object.
--> 703 httplib_response = self._make_request(
   conn,
   method,
   url,
   timeout=timeout_obj,
   body=body,
   headers=headers,
   chunked=chunked,
)
# If we're going to release the connection in ``finally:``, then
# the response doesn't need to know about the connection. Otherwise
# it will also try to release it and we'll have a double-release
# mess.
response_conn = conn if not release_conn else None

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/urllib3/connectionpool.py:449, in HTTPConnectionPool._make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
           httplib_response = conn.getresponse()
       except BaseException as e:
           # Remove the TypeError from the exception chain in
           # Python 3 (including for exceptions like SystemExit).
           # Otherwise it looks like a bug in the code.
--> 449             six.raise_from(e, None)
except (SocketTimeout, BaseSSLError, SocketError) as e:
   self._raise_timeout(err=e, url=url, timeout_value=read_timeout)

File <string>:3, in raise_from(value, from_value)

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/urllib3/connectionpool.py:444, in HTTPConnectionPool._make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
except TypeError:
   # Python 3
   try:
--> 444         httplib_response = conn.getresponse()
   except BaseException as e:
       # Remove the TypeError from the exception chain in
       # Python 3 (including for exceptions like SystemExit).
       # Otherwise it looks like a bug in the code.
       six.raise_from(e, None)

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/http/client.py:1348, in HTTPConnection.getresponse(self)
try:
   try:
-> 1348         response.begin()
   except ConnectionError:
       self.close()

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/http/client.py:316, in HTTPResponse.begin(self)
# read until we get a non-100 response
while True:
--> 316     version, status, reason = self._read_status()
   if status != CONTINUE:
       break

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/http/client.py:277, in HTTPResponse._read_status(self)
def _read_status(self):
--> 277     line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
   if len(line) > _MAXLINE:
       raise LineTooLong("status line")

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/socket.py:669, in SocketIO.readinto(self, b)
while True:
   try:
--> 669         return self._sock.recv_into(b)
   except timeout:
       self._timeout_occurred = True

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/ssl.py:1241, in SSLSocket.recv_into(self, buffer, nbytes, flags)
   if flags != 0:
       raise ValueError(
         "non-zero flags not allowed in calls to recv_into() on %s" %
         self.__class__)
-> 1241     return self.read(nbytes, buffer)
else:
   return super().recv_into(buffer, nbytes, flags)

File /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/ssl.py:1099, in SSLSocket.read(self, len, buffer)
try:
   if buffer is not None:
-> 1099         return self._sslobj.read(len, buffer)
   else:
       return self._sslobj.read(len)

KeyboardInterrupt: 

To ensure we really only get the area that we want, we use geopandas’s clip function to only keep the area we want. This function does exactly the same as the clip function in QGIS.

landuse = landuse.clip(area)

When we want to visualize or analyse the data, we want all information in a single column. However, at the moment, all information that was tagged as “natural”, has no information stored in the “landuse” tags. It is, however, very convenient if we can just use a single column for further exploration of the data.

To overcome this issue, we need to add the missing information to the landuse column, as done below.

landuse.loc[landuse.natural=='water','landuse'] = 'water'
landuse.loc[landuse.natural=='beach','landuse'] = 'beach'
landuse.loc[landuse.natural=='grassland','landuse'] = 'grass'
landuse.loc[landuse.natural=='wetland','landuse'] = 'wetlands'
landuse = landuse.dropna(subset=['landuse'])

Our next step is to prepare the visualisation of a map. What better way to explore land-use information than plotting it on a map? As you will see below, we can create a dictionary with color codes that will color each land-use class based on the color code provided in this dictionary.

color_dict = {  "grass":'#c3eead',               "railway": "#000000",
                "forest":'#1c7426',              "orchard":'#fe6729',
                "residential":'#f13013',         "industrial":'#0f045c',
                "retail":'#b71456',              "education":'#d61181',              
                "commercial":'#981cb8',          "farmland":'#fcfcb9',
                "cemetery":'#c39797',            "construction":'#c0c0c0',
                "meadow":'#c3eead',              "farmyard":'#fcfcb9',
                "plant_nursery":'#eaffe2',       "scrub":'#98574d',
                "allotments":'#fbffe2',          "reservoir":'#8af4f2',
                "static_caravan":'#ff3a55',      "wetlands": "#c9f5e5",
                "water": "#c9e5f5",              "beach": "#ffeead",
                "landfill" : "#B08C4D",          "recreation_ground" : "#c3eead",
                "brownfield" : "#B08C4D",        "village_green" : "#f13013" ,
                "military": "#52514E"}

Unfortunately, OpenSteetMap very often contains elements that have a unique tag. As such, it may be the case that some of our land-use categories are not in the dictionary yet.

Let’s first create an overview of the unique land-use categories within our data:

landuse.landuse.unique()

array(['water', 'forest', 'grass', 'residential', 'farmland',
       'industrial', 'landfill', 'allotments', 'cemetery',
       'village_green', 'meadow', 'recreation_ground', 'railway',
       'brownfield', 'commercial', 'retail', 'wetlands', 'military',
       'beach'], dtype=object)

Ofcourse we can visually compare the array above with our color_dict, but it is much quicker to use Sets to check if there is anything missing:

set(landuse.landuse.unique())-set(color_dict)

set()

In case anything is missing, add them to the color_dict dictionairy and re-run that cell. You can find new hexcodes here: https://htmlcolorcodes.com/color-picker/. You can also use that website later on if you disagree with some of the color codes we already added to this dictionairy.

Our next step is to make sure that we can connect our color codes to our dataframe with land-use categories.

color_dict = {key: color_dict[key]
             for key in color_dict if key not in  list(set(color_dict)-set(landuse.landuse.unique()))}

map_dict = dict(zip(color_dict.keys(),[x for x in range(len(color_dict))]))

landuse['col_landuse'] = landuse.landuse.apply(lambda x: color_dict[x])

Now we can plot the figure!

As you will see in the cell below, we first state that we want to create a figure with a specific figure size. You can change the dimensions to your liking.

fig, ax = plt.subplots(1, 1,figsize=(12,10))

# add color scheme
color_scheme_map = list(color_dict.values())
cmap = LinearSegmentedColormap.from_list(name='landuse',
                                     colors=color_scheme_map)  

# and plot the land-use map.
landuse.plot(color=landuse['col_landuse'],ax=ax,linewidth=0)

# remove the ax labels
ax.set_xticks([])
ax.set_yticks([])
ax.set_axis_off()

# add a legend:
legend_elements = []
for iter_,item in enumerate(color_dict):
    legend_elements.append(Patch(facecolor=color_scheme_map[iter_],label=item))        

ax.legend(handles=legend_elements,edgecolor='black',facecolor='#fefdfd',prop={'size':12},loc=(1.02,0.2)) 

# add a title
ax.set_title(place_name,fontweight='bold')

Text(0.5, 1.0, 'Steenwijk, The Netherlands')

3. Rasterize land-use information#

As you have noticed already during the lecture, and as we will again see next week when using the Google Earth Engine, most land-use data is in raster format.

In OpenStreetMap, as you have just noticed, the land-use information is in vector format. While it is not always necessary to have this information in raster format, it is useful to know how to get your info.

To do so, we make use of the GeoCube package, which is a newly developed Python package that can very easily convert vector data into a raster format.

The first thing we will need to do is to define all the unique land-use classes and store them in a dictionary:

categorical_enums = {'landuse': landuse.landuse.drop_duplicates().values.tolist()
}

And now we simply use the make_geocube() function to convert our vector data into raster data.

out_grid = make_geocube(
    vector_data=landuse,
    output_crs="epsg:4326",
    resolution=(-0.0001, 0.0001),
    categorical_enums=categorical_enums
)

Let’s explore what this has given us:

out_grid["landuse"]

<xarray.DataArray 'landuse' (y: 391, x: 775)>
array([[-1, -1, -1, ..., -1, -1, -1],
       [-1, -1, -1, ..., -1, -1, -1],
       [-1, -1, -1, ..., -1, -1, -1],
       ...,
       [-1, -1, -1, ..., -1, -1, -1],
       [-1, -1, -1, ..., -1, -1, -1],
       [-1, -1, -1, ..., -1, -1, -1]], dtype=int16)
Coordinates:
  * y            (y) float64 52.81 52.81 52.81 52.81 ... 52.77 52.77 52.77 52.77
  * x            (x) float64 6.085 6.085 6.085 6.085 ... 6.162 6.162 6.162 6.162
    spatial_ref  int32 0
Attributes:
    name:                 landuse
    long_name:            landuse
    _FillValue:           -1
    categorical_mapping:  landuse_categories

Let’s plot it to see the result!

fig, ax = plt.subplots(1, 1,figsize=(14,10))

out_grid["landuse"].plot(ax=ax,vmin=0,vmax=15)

# remove the ax labels
ax.set_xticks([])
ax.set_yticks([])
ax.set_axis_off()

ax.set_title('Rasterized land-use map for {}'.format(place_name))

Text(0.5, 1.0, 'Rasterized land-use map for Steenwijk, The Netherlands')

As we can see in the figure above, the land-use categories have turned into numbers, instead of land-use categories described by a string value.

This is of course a lot harder to interpret. Let’s try to color these the same way as our previous land-use map.

4. Extracting buildings from OpenStreetMap#

There is a lot more data to extract from OpenStreetMap besides land-use information. Let’s extract some building data. To do so, we use the “building” tag.

tags = {"building": True}
buildings = ox.geometries_from_place(place_name, tags)

Now let’s see what information is actually extracted:

buildings.head()

		addr:city	addr:housenumber	addr:postcode	addr:street	building	name	source	source:date	geometry	emergency	...	website	social_facility	social_facility:for	designation	substation	note:BAG	bridge:support	construction	ways	type
element_type	osmid
node	2823618780	Steenwijk	15	8332JD	Parallelweg	public	Wijkcentrum De Oerthe	BAG	2014-03-24	POINT (6.13482 52.78944)	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
way	58337006	NaN	NaN	NaN	NaN	yes	NaN	3dShapes	NaN	POLYGON ((6.10044 52.78450, 6.10086 52.78442, ...	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	58343657	NaN	NaN	NaN	NaN	roof	NaN	NaN	NaN	POLYGON ((6.12222 52.79196, 6.12202 52.79200, ...	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	265889057	NaN	NaN	NaN	NaN	yes	Dierenkliniek Steenwijk	BAG	2014-02-11	POLYGON ((6.12290 52.79493, 6.12284 52.79494, ...	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	265889058	Steenwijk	21	8332JE	Woldmeentherand	yes	McDonald's	BAG	2020-01-02	POLYGON ((6.12035 52.79910, 6.12013 52.79915, ...	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 64 columns

As you notice in the output of the cell above, there are many columns which just contain “NaN”. And there even seem to be to many columns to even visualize properly in one view.

Let’s check what information is collected for the different buildings:

buildings.columns

Index(['addr:city', 'addr:housenumber', 'addr:postcode', 'addr:street',
       'building', 'name', 'source', 'source:date', 'geometry', 'emergency',
       'opening_hours', 'nodes', 'man_made', 'layer', 'amenity', 'ref:bag',
       'start_date', 'addr:country', 'brand', 'brand:wikidata',
       'brand:wikipedia', 'contact:phone', 'contact:website', 'cuisine',
       'delivery', 'description', 'drive_through', 'internet_access',
       'internet_access:fee', 'takeaway', 'toilets:wheelchair', 'wheelchair',
       'religion', 'heritage', 'ref:bag:old', 'power', 'shop',
       'building:levels', 'addr:housename', 'architect', 'architect:wikidata',
       'building:architecture', 'heritage:operator', 'historic', 'note',
       'ref:rce', 'survey:date', 'wikidata', 'wikipedia', 'year',
       'roof:levels', 'official_name', 'operator', 'denomination', 'website',
       'social_facility', 'social_facility:for', 'designation', 'substation',
       'note:BAG', 'bridge:support', 'construction', 'ways', 'type'],
      dtype='object')

5. Analyze and visualize building stock#

What we also noticed is that quite some buildings are identified as ‘yes’. This is not very useful as it does not really say much about the use of the building.

Let’s see for how many buildings this is the case:

buildings.building.value_counts()

house             6449
yes               4617
industrial         218
apartments         174
retail              72
construction        59
commercial          56
static_caravan      23
roof                11
houseboat            6
shed                 5
office               5
school               3
church               3
civic                1
service              1
public               1
Name: building, dtype: int64

Now let’s visualize the buildings again. We need to create a similar color dictionary as we did for the land-use categories. Now its up to you to make it!

color_dict = {  "grass":'#c3eead',               "railway": "#000000",
                "forest":'#1c7426',              "orchard":'#fe6729',
                "residential":'#f13013',         "industrial":'#0f045c',
                "retail":'#b71456',              "education":'#d61181',              
                "commercial":'#981cb8',          "farmland":'#fcfcb9',
                "cemetery":'#c39797',            "construction":'#c0c0c0',
                "meadow":'#c3eead',              "farmyard":'#fcfcb9',
                "plant_nursery":'#eaffe2',       "scrub":'#98574d',
                "allotments":'#fbffe2',          "reservoir":'#8af4f2',
                "static_caravan":'#ff3a55',      "wetlands": "#c9f5e5",
                "water": "#c9e5f5",              "beach": "#ffeead",}


# Remove multiple keys from dictionary
color_dict = {key: color_dict[key]
             for key in color_dict if key not in  list(set(color_dict)-set(landuse.landuse.unique()))}

map_dict = dict(zip(color_dict.keys(),[x for x in range(len(color_dict))]))

And plot the figure in the same manner!

# Plot the figure
fig, ax = plt.subplots(1, 1,figsize=(12,10))
color_scheme_map = list(color_dict.values())

cmap = LinearSegmentedColormap.from_list(name='landuse',
                                     colors=color_scheme_map)  

buildings.plot(column='building',ax=ax,linewidth=0)

ax.set_xticks([])
ax.set_yticks([])
ax.set_axis_off()
#legend_elements = []
#for iter_,item in enumerate(color_dict):
#    legend_elements.append(Patch(facecolor=color_scheme_map[iter_],label=item))        

#ax.legend(handles=legend_elements,edgecolor='black',facecolor='#fefdfd',prop={'size':12},loc=(1.02,0.2)) 

ax.set_title(place_name,fontweight='bold')

Text(0.5, 1.0, 'Steenwijk, The Netherlands')

6. Extracting roads from OpenStreetMap#

Let’s continue (and end) this tutorial with the core data in OpenStreetMap (it is even in the name): roads!

Now, instead of using tags, we want to identify what type of roads we would like to extract. Let’s first only extract roads that can be used to drive.

G = ox.graph_from_place(place_name, network_type="drive")

# plot the street network with folium
m1 = ox.plot_graph_folium(G, popup_attribute="name", weight=2, color="#8b0000")

m1

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7. Plot Routes Using OpenStreetMap and Folium#

One of the exiting things we can do with this data is that we can compute and plot routes between two points on a map.

# use networkx to calculate the shortest path between two nodes
origin_node = list(G.nodes())[0]
destination_node = list(G.nodes())[-1]
route = nx.shortest_path(G, origin_node, destination_node)

# plot the route with folium
# like above, you can pass keyword args along to folium PolyLine to style the lines
m2 = ox.plot_route_folium(G, route, weight=10)

m2

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# plot the route with folium on top of the previously created graph_map
m3 = ox.plot_route_folium(G, route, route_map=m1, popup_attribute="length", weight=7)

# save as html file then display map as an iframe
m3

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Big Data for Sustainability Sciences

Tutorial 1: Working with OpenStreetMap

Contents

Tutorial 1: Working with OpenStreetMap#

Tutorial Outline

Learning Objectives#

1.Introducing the packages#

2. Extract and visualize land-use information from OpenStreetMap#

3. Rasterize land-use information#

4. Extracting buildings from OpenStreetMap#

5. Analyze and visualize building stock#

6. Extracting roads from OpenStreetMap#

7. Plot Routes Using OpenStreetMap and Folium#